From Data to Deployment — Learn MLOps by Building a Real-World Machine Learning Project with MLflow, Docker, Kubernetes

What you'll learn
Build end-to-end Machine Learning pipelines with MLOps best practices
Understand and implement ML lifecycle from data engineering to model deployment
Set up MLFlow for experiment tracking and model versioning
Package and serve models using FastAPI and Docker
Automate workflows using GitHub Actions for CI pipelines
Deploy inference infrastructure on Kubernetes using KIND
Use Streamlit for building lightweight ML web interfaces
Learn GitOps-based CD pipelines using ArgoCD
Serve models in production using Seldon Core
Monitor models with Prometheus and Grafana for production insights
Understand handoff workflows between Data Science, ML Engineering, and DevOps
Build foundational skills to transition from DevOps to MLOps roles

Requirements
Basic knowledge of DevOps and Docker
Familiarity with Git and GitHub
Some exposure to Python (used for scripting and ML workflows)
Prior understanding of CI/CD concepts is helpful but not mandatory
A machine with minimum 8GB RAM and Docker installed for running local labs

Description
This hands-on bootcamp is designed to help DevOps Engineers and infrastructure professionals transition into the growing field of MLOps. With AI/ML rapidly becoming an integral part of modern applications, MLOps has emerged as the critical bridge between machine learning models and production systems.In this course, you will work on a real-world regression use case — predicting house prices — and take it all the way from data processing to production deployment on Kubernetes. You'll start by setting up your environment using Docker and MLFlow for tracking experiments. You'll understand the machine learning lifecycle and get hands-on experience with data engineering, feature engineering, and model experimentation using Jupyter notebooks.Next, you'll package the model with FastAPI and deploy it alongside a Streamlit-based UI. You'll write GitHub Actions workflows to automate your ML pipeline for CI and use DockerHub to push your model containers.In the later stages, you'll build a scalable inference infrastructure using Kubernetes, expose services, and connect frontends and backends using service discovery. You'll explore production-grade model serving with Seldon Core and monitor your deployments with Prometheus and Grafana dashboards.Finally, you'll explore GitOps-based continuous delivery using ArgoCD to manage and deploy changes to your Kubernetes cluster in a clean and automated way.By the end of this course, you'll be equipped with the knowledge and hands-on experience to operate and automate machine learning workflows using DevOps practices — making you job-ready for MLOps and AI Platform Engineering roles.

Who this course is for:
DevOps Engineers looking to break into the field of MLOps, Platform Engineers and SREs supporting ML teams, Cloud Engineers wanting to understand ML workflows and productionization, Developers transitioning into ML Engineering or Data Engineering roles, Anyone curious about how real-world ML systems are deployed and scaled

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